Nanomaterials-Based Electrochemical Δ9-THC and CBD Sensors for Chronic Pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chronic pain is now included in the designation of chronic diseases, such as cancer, diabetes, and cardiovascular disease, which can impair quality of life and are major causes of death and disability worldwide. Pain can be treated using cannabinoids such as Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) due to their wide range of therapeutic benefits, particularly as sedatives, analgesics, neuroprotective agents, or anti-cancer medicines. While little is known about the pharmacokinetics of these compounds, there is increasing interest in the scientific understanding of the benefits and clinical applications of cannabinoids. In this review, we study the use of nanomaterial-based electrochemical sensing for detecting Δ9-THC and CBD. We investigate how nanomaterials can be functionalized to obtain highly sensitive and selective electrochemical sensors for detecting Δ9-THC and CBD. Additionally, we discuss the impacts of sensor pretreatment at fixed potentials and physiochemical parameters of the sensing medium, such as pH, on the electrochemical performance of Δ9-THC and CBD sensors. We believe this review will serve as a guideline for developing Δ9-THC and CBD electrochemical sensors for point-of-care applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it